However, accuracy is not the sole criterion for choosing a classifier: in many real-world applications,
another important criterion is intelligibility.
Roughly speaking, a classifier is intelligible if its predictions
can be explained in understandable terms to a user, and if its behavior can
be verified according to the user's expectations. The explainability requirement
is a legal issue in Europe since the implementation of the General Data Protection Regulation (EU) 2016/679 (“GDPR”)
on May 25th, 2018 [1]}. Accordingly, explainable and robust AI (XAI) has become a very active research
topic for the past couple of years (see e.g. [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}).
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